数据与 AI 架构师
Data & AI Architect
职位概述
该架构师将负责合同前的技术方案设计及后续交付，确保从提案到生产环境的连续性。加入一个小型、资深的团队意味着早期接触客户，快速做出架构决策，亲自参与实施，并直接对交付标准和可重用资产产生影响。这也意味着工作内容多样且存在不确定性，有时需要制定操作手册；寻求明确职责范围的候选人可能不适合该职位。时间大约平均分配在客户参与和交付上，根据项目进展灵活调整。典型工作包括发现研讨会、目标状态架构、提案估算、代码审查、构建检索增强的垂直切片、客户赋能、指导委员会汇报以及将经验转化为可重用模式。
职位职责：
解决方案设计与售前支持
- 设计技术方案，挑战问题陈述并组织发现研讨会。
- 生成目标状态架构、构建顺序、提案假设、排除项、风险和有说服力的估算。
- 设计四到六周的原型验证，并作为技术同行与客户架构师、数据负责人和首席信息官合作。
交付与实际架构
- 负责端到端架构，并在需要时领导交付、范围、站会和客户技术沟通。
- 保持动手实践，实现复杂组件和参考解决方案。
- 交付 Databricks lakehouses，包括银层、Unity Catalog、Delta Lake、数据摄入和编排。
- 构建生产级生成式 AI 系统，涵盖 RAG、代理、评估、提示/上下文工程、成本和延迟。
- 建立 CI/CD、基础设施即代码、测试、可观测性和成本标准；指导客户工程师并管理生产准备和交接。
团队能力与知识产权
- 将交付经验转化为参考架构、加速器、模板和估算模型。
- 为 Frontier Academy 做出贡献，并维护 Databricks、Microsoft 和 Anthropic 的当前建议。
- 协助塑造并最终领导一个小的交付团队，包括招聘工作。
任职要求：
必须具备：
- 八年左右的数据/AI 工程和架构经验，包括三年以上具有实质性设计权和高级客户面向咨询经验。
- Databricks：lakehouse 架构、Delta Lake、Unity Catalog、Spark/PySpark、Lakeflow 或 Delta Live Tables、编排
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JOB SUMMARY
The architect will lead both pre-contract technical solutioning and subsequent delivery, preserving continuity from proposal to production. Joining a small, senior practice means early client exposure, fast architectural decisions, hands-on implementation and direct influence over delivery standards and reusable assets. It also means varied, ambiguous work and occasionally creating the playbook; candidates seeking a tightly defined remit may not find the role suitable. Time is split approximately equally between client engagement and delivery, flexing with the pipeline. Typical work includes discovery workshops, target-state architecture, proposal estimates, code review, building a retrieval-augmented vertical slice, client enablement, steering-committee presentations and converting lessons into reusable patterns.
JOB RESPONSIBILITIES:
Solutioning and pre-sales
- Shape technical approaches, challenge problem statements and facilitate discovery workshops.
- Produce target-state architectures, build sequences, proposal assumptions, exclusions, risks and defensible estimates.
- Design four-to-six-week proofs of concept and serve as technical peer to client architects, data leaders and CIOs.
Delivery and hands-on architecture
- Own end-to-end architecture and, where required, lead delivery, scope, stand-ups and client technical
- Remain hands-on, implementing demanding components and reference solutions.
- Deliver Databricks lakehouses, including medallion layers, Unity Catalog, Delta Lake, ingestion and orchestration.
- Build production generative AI systems covering RAG, agents, evaluation, prompt/context engineering, cost and latency.
- Set CI/CD, infrastructure-as-code, testing, observability and cost standards; mentor client engineers and manage production readiness and handover.
Practice capability and intellectual property
- Turn delivery experience into reference architectures, accelerators, templates and estimation models.
- Contribute to Frontier Academy and maintain current recommendations across Databricks, Microsoft and Anthropic.
- Help shape and eventually lead a small delivery team, including recruitment.
JOB QUALIFICATIONS:
Must Have:
- About eight years in data/AI engineering and architecture, including three years with substantive design authority and senior client-facing consulting exposure.
- Databricks: lakehouse architecture, Delta Lake, Unity Catalog, Spark/PySpark, Lakeflow or Delta Live Tables, orchestration, performance and cost optimisation.
- Azure/Microsoft: Data Factory or Fabric, ADLS, Azure OpenAI or AI Foundry, Entra ID and networking
- Generative AI: production RAG, vector stores, agents/tool use, evaluation, guardrails and prompt/context engineering, including Claude or an equivalent frontier model.
- Strong production Python and SQL; sound data-modelling judgement across dimensional, data vault and wide denormalised approaches.
- DevOps/MLOps fundamentals: version control, CI/CD, infrastructure as code, containers and monitoring.
- Excellent written and spoken English for executive proposals, decision records and presentations.
Desirable:
- Databricks Professional or Azure Solutions Architect Expert certification.
- Big Four, global systems integrator or specialist consultancy experience, including bids, statements of work and estimation.
- Applied responsible AI governance and delivery experience in financial services, retail or travel.
- Experience with Australian/APAC clients, Snowflake, dbt, Power BI or Fabric, and mentoring small engineering teams.
Originally posted on Himalayas